Amazon has 1472 active AI-related job listings. The company is heavily focused on roles within the "agents" stage, which accounts for 38% of its AI hiring, followed by "application" at 26%. Engineering is the dominant function, with 1172 positions. Over the last 30 days, Amazon has added 667 new AI roles, representing a 74% increase compared to the previous 30-day period. Frequent tech tags include agent_orchestration, model_serving, and multimodal.
Currently tracking 1109 active AI roles, down 11% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
Amazon currently has 1575 active AI-related roles in our index. The most common open titles are: ML Data Associate-II (9), 2026 Applied Scientist Intern, Amazon University Talent Acquisition (8), AI Data Associate (Dutch) , Artificial General Intelligence Data Services (8), Senior Delivery Consultant - Data , Professional Services, AWSI HCLS (8), Software Development Engineer, AWS (8). Most positions are in Engineering and Research.
Amazon's active AI hiring is concentrated in: agents (41%), application (26%), serving infrastructure (13%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Amazon is hiring AI talent in: United States (1022 roles), Canada (59 roles), United Kingdom (49 roles), India (23 roles).
Job postings at Amazon most frequently mention: Machine Learning, Generative AI, Large Language Models (LLMs), Software Engineering, Agentic Systems.
In the past 30 days, Amazon has posted 732 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Applied Scientist, Grocery, Retail & In-Store Experience (GRAISE) Applied Scientist role focused on designing, developing, and deploying machine learning and computer vision models for Amazon's in-store grocery technologies. The role involves end-to-end model development, from ideation to production, with a focus on solving complex grocery-domain problems at scale and improving the customer shopping experience. | ShipServe | 8 |
| Applied Scientist II, Payment Risk Machine Learning Applied Scientist II role focused on building and deploying machine learning models and agentic AI systems for payment risk management and fraud detection at Amazon. The role involves end-to-end development, from data analysis and model design to production deployment and monitoring, utilizing techniques like deep learning, LLMs, graph neural networks, and multi-agent systems. |
| AgentServe |
| 8 |
| Software Development Manager - AWS Glue, Glue and GenAI for Data Processing Software Development Manager to lead a team building agentic AI systems for AWS Glue, EMR, and Athena, focusing on automated Spark upgrade and migration agents, and a managed analytics service providing AI assistants and agents access to tools. The role involves owning design, implementation, testing, and deployment, driving technical decisions at the intersection of GenAI, distributed systems, big data, and ML, and partnering with senior leadership and customers. | Agent | 8 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing ML kernel performance for AWS Neuron SDK on custom ML accelerators (Inferentia and Trainium). It involves designing and implementing high-performance compute kernels, analyzing and optimizing kernel-level performance, implementing compiler optimizations, and collaborating with customers and internal teams to enable and optimize ML models. The work is at the hardware-software boundary, combining deep hardware knowledge with ML expertise. | Serve | 8 |
| Senior Forward Deployed Deep Learning Architect, Generative AI Innovation Center Senior Deep Learning Architect focused on implementing and fine-tuning Generative AI solutions for AWS customers, involving customer interaction, solution design, experimentation, and providing guidance on adoption and best practices. The role bridges customer needs with technical implementation and feedback to product teams. | ShipPost-train | 8 |
| Applied Scientist Intern, 2026 Shenzhen This internship focuses on bridging cutting-edge AI research with practical application and communication. The intern will translate complex AI concepts into understandable content for business stakeholders and the wider community, document AI capabilities, develop internal AI literacy programs, and contribute to applied research projects in NLP, Computer Vision, or Multimodal AI. The role requires a strong foundation in ML/DL, Python, and ML frameworks, with a passion for science communication and a curious, open mindset. | Post-trainAgent | 8 |
| Principal GTM Specialist Solution Architect AI/ML, GenAI, EMEA Data and AI Solutions Architecture Principal Solutions Architect specializing in AI/ML and GenAI for EMEA customers, focusing on designing and implementing large-scale AI/ML initiatives, enterprise-wide generative AI platforms, agentic systems, and MLOps frameworks. The role involves influencing AI strategy at C-suite levels, guiding customers to production, and contributing to AWS service roadmaps. | AgentServe | 8 |
| Applied Scientist II, AERO Agentic AI Team The Applied Scientist II role on the AERO Agentic AI Team at Amazon focuses on developing Agentic AI solutions for Selling Partners and Retail users. The role involves working with LLMs and agentic technologies to improve user experience, build large-scale systems, and advance the state-of-the-art in Generative and Agentic AI. | Agent | 8 |
| Applied Scientist II, Amazon Business, Amazon Business - GTMO Science Applied Scientist II role at Amazon Business focused on revolutionizing sales productivity using AI-powered solutions. The role involves developing tools for Account Executives (AEs) to prioritize accounts, recommend products, and engage customers more effectively. It leverages machine learning and Generative AI to outreach customers based on their behavior and purchase history, and performs text mining on customer conversations to recommend solutions. The scientist will partner with product, tech, and sales teams to launch and scale global AI products, with a focus on improving customer experience and sales efficiency. | AgentData | 8 |
| Software Engineer II- AI/ML, AWS Neuron Software Engineer II role focused on optimizing and enabling deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia and Trainium) by developing and enhancing the AWS Neuron SDK. This involves working across the stack from frameworks like PyTorch/JAX to hardware-software boundaries, optimizing ML compilers, runtimes, and high-performance kernels for inference and training. The role requires strong software development skills in Python/C++, system-level programming, ML knowledge, and collaboration with various teams to ensure optimal performance for customers. | ServePost-train | 8 |
| Principal GenAI Specialist SA This role is for a Principal GenAI Specialist SA at Amazon, focusing on designing and architecting scalable, secure, and cost-effective AI/ML, Generative AI, and Agentic AI solutions on AWS. The role involves guiding customers through their AI transformation, establishing GenAIOps practices, and creating enterprise-grade AI architectures. It requires deep technical experience across the AI spectrum, including LLM customization/fine-tuning, inference optimization, agentic frameworks, GenAIOps, security, RAG systems, and prompt engineering. | Agent | 8 |
| Applied Scientist, Devices & Services Applied Scientist role focused on developing and implementing AI, computer vision, machine learning, and robotics solutions for customer-facing products and devices. The role involves establishing scalable processes for data analysis and model development, specifying and implementing robotic system functionality, and collaborating with cross-functional teams to deliver innovative intelligent systems at scale. | Ship | 8 |
| Manager, Applied Science , Brand Protection ML Manager for an Applied Science team focused on Brand Protection ML at Amazon. The role involves leading scientists to build and launch scalable AI/ML/LLM/GenAI solutions to identify and prevent infringement and counterfeit on Amazon's platform globally. The team works on complex business problems with significant customer impact, leveraging SOTA ML techniques and deep learning, computer vision, and NLP. | ShipPost-train | 8 |
| Applied Scientist, Brand Protection Machine Learning Applied Scientist role focused on building and deploying Generative AI solutions for Brand Protection using NLP, computer vision, and LLMs. The role involves end-to-end ownership from conception to launch, collaborating with product and engineering teams, and analyzing data to solve complex business problems at scale. | Ship | 8 |
| Applied Scientist Applied Scientist role focused on developing and deploying production-ready AI/ML models for consumer-facing features like content understanding, recommendations, and GenAI applications. The role involves inventing new approaches, adapting existing ones, and building scalable, efficient solutions. It requires collaboration with scientists and engineers, with a focus on both scientific and engineering best practices, and potentially contributing to research papers. The role touches on inference infrastructure and model serving, with a primary focus on building agentic or product-level AI features. | AgentServe | 8 |
| Applied Scientist II, Console Science The Applied Scientist II will focus on building industry-leading Conversational AI Systems using Generative AI, LLMs, NLU, and Applied ML. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The team explores new technologies and finds creative solutions for AWS customers, working with foundation models and generative AI to reimagine customer experiences. | AgentPost-train | 8 |
| Sr. Security Consultant - GenAI, AWS GenAI Innovation and Delivery This role is for a Senior GenAI Security Consultant who will embed directly with Forward Deployed Engineering (FDE) teams to build and implement secure GenAI solutions. The focus is on hands-on coding and architecture to ensure security by design, rather than a strategy role. Responsibilities include co-developing solutions, enforcing security standards, architecting and implementing controls, translating requirements into code, and serving as a technical security authority. The role also involves building innovative GenAI-powered security solutions and contributing to thought leadership. | Agent | 8 |
| Software Development Engineer II, Items and Relationships Platform Software Development Engineer II role focused on building and optimizing GenAI serving systems and ML platforms at massive scale. The role involves working with LLMs, VLMs, and multimodal foundation models, including optimized model serving, distillation, quantization, distributed inference, vector indices, and agentic systems. The primary focus is on the engineering and infrastructure aspects of bringing AI models to production, with a secondary involvement in agentic systems. | ServeAgent | 8 |
| Applied Scientist, Amazon Compliance and Safety Services Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research. | Post-trainData | 8 |
| Applied Scientist, Amazon Compliance and Safety Services Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research. | Post-trainData | 8 |
| Sr Innovation Engineer, IHub, Innovation and Engagement Senior Innovation Engineer role focused on building end-to-end, executive-grade AI/ML demos and prototypes for the AWS APJ Innovation Hub. The role involves rapid prototyping, architecting serverless solutions, integrating various AWS AI/ML services (Bedrock, SageMaker, Bedrock Agents), and developing reusable components and templates to accelerate future builds. Emphasis on showcasing AWS capabilities, staying current with new service launches, and ensuring prototypes meet high UX and operational standards. | Agent | 8 |
| Applied Scientist II, RBS Tech The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research. | AgentPost-train | 8 |
| Data Scientist, Demand Forecasting Research Scientist role focused on building and deploying large-scale foundation models for demand forecasting at Amazon. The role involves designing experiments, developing deep learning and statistical models, and analyzing large datasets to improve forecasting accuracy and downstream business impact. Emphasis on research rigor, production deployment, and scientific contribution. | Post-train | 8 |
| Applied Scientist, Last Mile Delivery Automation This role focuses on developing AI and ML solutions for last mile delivery automation, combining expertise in machine learning, computer vision, and robotics to solve complex challenges in perception, navigation, and path planning. The scientist will research, design, and implement algorithms, transforming research concepts into production-ready solutions for autonomous systems. | ShipAgent | 8 |
| Sr Applied Scientist, Applied AI Solutions Senior Applied Scientist role focused on building agentic AI products for businesses, involving end-to-end GenAI project ownership, ML model development and deployment, and research into innovative ML approaches. The role emphasizes building multi-agent systems using techniques like fine-tuning and reinforcement learning, with a focus on customer-facing features and scalable solutions. | AgentPost-train | 8 |
| Applied Scientist II, Trustworthy Shopping Experience (TSE) Applied Scientist II role focused on building and deploying Generative AI solutions for Amazon's Trustworthy Shopping Experience (TSE) team. The role involves creating intelligent systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, leveraging techniques like SFT and RFT. It requires end-to-end ownership from research to production, impacting millions of customers. | AgentPost-train | 8 |
| Senior AI Solution Architect Senior AI Solution Architect for AWS, focusing on helping customers adopt and scale GenAI/ML and Agentic technologies in production. This role involves building technical relationships, designing scalable architectures, providing expert guidance on AWS AI services, and contributing to the development of best practices and technical content. | AgentServe | 8 |
| Senior AI Solution Architect Senior AI Solution Architect for AWS, focusing on guiding customers in adopting and scaling GenAI/ML and Agentic technologies. The role involves designing technical architectures, advising on best practices, and acting as a trusted advisor for complex AI projects, with a strong emphasis on production environments and AWS services. | AgentServe | 8 |
| Senior AI Solution Architect Senior AI Solution Architect for AWS, focusing on helping enterprise customers adopt and scale GenAI/ML and Agentic technologies. The role involves designing technical architectures, advising on best practices, and acting as a trusted advisor for complex AI projects, with a strong emphasis on production deployment and operational efficiency. | AgentServe | 8 |
| Applied Scientist, PRG (Personal Robotics Group) This role focuses on researching and developing advanced navigation systems for intelligent robotic products, utilizing a spectrum of approaches from classical methods to learning-based techniques and foundation models. The primary goal is to enable robots to move reliably and safely in complex, dynamic environments, with a strong emphasis on sim-to-real transfer and evaluation frameworks. | AgentData | 8 |
| Machine Learning Scientist - GenAI, KIT Machine Learning Scientist role focused on Generative AI within AWS, aiming to identify customer needs and improve cloud adoption. The role involves building Agentic AI systems, fine-tuning LLMs, applying Reinforcement Learning, and generating insights from large datasets, with a focus on taking ideas from conception to production. | AgentPost-train | 8 |
| Applied Scientist II, Alexa AI Applied Scientist II at Amazon Alexa AI focused on prototyping, optimizing, and deploying ML algorithms in Generative AI. Responsibilities include research, building PoCs, collaborating with teams, technical communication, documentation, and publishing research. | Post-train | 8 |
| Senior Solutions Architect - Telco Customer Experience Transformation, AWS Industries, Telco Solutions Architect for AWS Telco customers, focusing on customer experience transformation using AI agents, conversational AI, and omnichannel orchestration. The role involves designing and building solutions with AWS services like Amazon Connect and Bedrock AgentCore, driving adoption, and influencing product roadmaps. | AgentServe | 8 |
| Senior Software Development Engineer - AI/ML, AWS Neuron, Multimodal Inference Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). The role involves designing, developing, and optimizing ML models and frameworks for deployment, with a strong emphasis on distributed inference, performance tuning (latency and throughput), and system-level optimizations for LLMs. | Serve | 8 |
| Data Scientist, SPX AI Lab, SPX Science Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale. | Agent | 8 |
| Senior Software Development Engineer , Stores Foundational AI - Rufus Senior Software Development Engineer focused on building and scaling foundational LLMs for Amazon Stores. The role involves architecting and building ML infrastructure for LLM training and post-training workflows (fine-tuning, RL, continuous learning), transforming customer interactions into training signals, optimizing RL systems, and partnering with scientists to productionize frontier techniques like RLHF and agentic workflows. Emphasis on end-to-end system ownership, including design, implementation, deployment, and observability, with a focus on low-level optimization like CUDA kernels and ML platforms. | Post-trainServe | 8 |
| Applied Scientist, Alexa Smart Properties Applied Scientist role focused on building LLM-driven conversational assistants for enterprise use cases in hospitality and senior living, leveraging Amazon's scale and data. Responsibilities include developing core LLM technologies, prompt optimization, and building/measuring metrics for these systems. | Agent | 8 |
| Applied Scientist, Selection Monitoring This role focuses on developing and deploying advanced ML/AI technologies for catalog expansion, including information extraction, website comprehension, and agentic systems for multi-step decision-making. It involves working with large-scale data, deep learning, NLP, and image processing to extract and structure information from various document types, with an emphasis on scalable solutions and leveraging recent advances in RL-based fine-tuning methods. | AgentData | 8 |
| Sr. Applied Scientist, Amazon Robotics, Structured Field Coordinated Planning & Control Senior Applied Scientist role focused on AI-driven structured field robotics, including path planning, fleet coordination, and control systems. The role involves leading research, translating breakthroughs into production solutions at scale, and owning end-to-end delivery of algorithmic solutions. It requires a PhD or Master's with significant experience in robotics, ML, and algorithm development, with a focus on publishing research and mentoring junior scientists. The team operates at the intersection of planning, algorithmic, and ML research with production systems. | AgentServe | 8 |
| Software Development Engineer, Seller Assistant, SPX Software Development Engineer role focused on building and launching production-grade, multi-agent GenAI systems for Amazon Seller Assistant. The role involves end-to-end ownership from customer insight to shipped product, operating at Amazon's scale. | Agent | 8 |
| Senior AI Solution Architect This role focuses on converting AI ambition into programs that can be delivered, operated, and scaled in production environments. The AI Specialist SA team builds technical relationships with customers of all sizes and operate as their trusted advisor, ensuring they get the most out of the cloud at every stage of their journey while adopting GenAI/ML and Agentic technologies across their organisation. You’ll manage the overall technical relationship between AWS and our customers, making recommendations on security, cost, performance, reliability and operational efficiency to accelerate their challenging GenAI/ML and Agentic projects. | AgentServe | 8 |
| Senior Software Engineer, Speech MLOps Senior Software Engineer focused on MLOps for speech synthesis and GenAI experiences, involving building and maintaining ML infrastructure for the entire lifecycle on AWS. | ServePost-train | 8 |
| Data Scientist II, RufusX Science UK This role focuses on developing and optimizing AI-driven conversational shopping experiences using ML, NLP, and multimodal technologies. The Data Scientist will work on agentic systems, information retrieval, recommender systems, and multimodal LLMs to improve customer journeys, analyze experiments, and collaborate on deploying production systems. The role involves handling large-scale data and contributing to both agent capabilities and the underlying inference infrastructure. | AgentServe | 8 |
| Applied Scientist II, Foundation Model, Industrial Robotics Group The Applied Scientist II role focuses on developing and improving machine learning systems for industrial robotics, specifically leveraging and adapting foundation models for tasks like perception, reasoning, and action. This involves fine-tuning, optimization, experimentation, and building evaluation frameworks, with a contribution to data and training workflows. The goal is to enable generalization, multi-modal learning, and skill acquisition in robots operating at Amazon's scale. | AgentData | 8 |
| Senior Applied Scientist, Alexa Ads Senior Applied Scientist role at Amazon focusing on Generative AI for Alexa Conversational Ads and Personalization. Responsibilities include defining scientific vision, leading ML projects, architecting large-scale ML systems, mentoring junior scientists, and collaborating with product/engineering. Requires experience in building ML models for business applications, ML/LLM fundamentals, and large-scale systems. Preferred experience in ad tech and building ML models for recommendations, ads ranking, personalization, or search. | ShipAgent | 8 |
| Applied Scientist, Alexa Ads Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating on production deployment. | ShipPost-train | 8 |
| Applied Scientist, End User Messaging, AWS Applied AI Solutions Core Services This role focuses on developing advanced machine learning approaches and agentic systems for trust and safety in AWS cloud communication services. The primary goal is to create behavioral detection models and intelligent resource allocation algorithms that adapt to evolving threats and optimize service delivery. The role involves researching novel AI agent applications in security, integrating science components into production, and conducting rigorous experimentation. | Agent | 8 |
| Applied Scientist, Geospatial & Safety Science Applied Scientist role focused on leveraging computer vision, generative AI, and deep learning to enhance vehicle navigation and ensure safe, efficient deliveries by analyzing multimodal data. The role involves building large-scale ML systems, translating business requirements into prototypes, and optimizing models for production and edge devices. | ShipPost-train | 8 |
| AI Principal Product Manager-Technical, Alexa Responsible AI The AI Principal PMT for Alexa Responsible AI will define the standard for how Alexa earns and keeps customer trust. This role owns the product discipline of Responsible AI, defining customer experiences for safety guardrails, trust signals, and evaluation frameworks. The PMT will set product vision and strategy, lead cross-functional alignment across Applied Science, Engineering, Legal, Policy, and UX, and ensure the full responsible product experience including safety, privacy, and security. The role requires technical depth in LLMs and AI safety, understanding how models fail and writing requirements for safety model development and evaluation system design. The PMT will also mentor other PMs and influence Responsible AI scaling across Alexa. | Eval GatePost-train | 8 |
| Applied Scientist, Sales AI This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions. | AgentPost-train | 8 |
